Improving the range of EVs through urban data

Increasing the market penetration of electric vehicles is necessary to reduce negative externalities of mobility. This penetration is still small thanks in part to range anxiety. Gaps in the academic understanding of energy consumption of electric vehicles have been found, increasing this range anxiety. New ICT developments and the availability of data made it possible to create complex prediction models in order to gain more insights in the energy consumption. These insights could on the short-term lead to better range predictions, while on the long term they could be the basis of sustainability strategies and new business models. This research gives both qualitative and quantitative insight in the influence of driving styles, environmental variables, infrastructural design and traffic intensity on the energy consumption of electric vehicles. An energy consumption model has been used to calculate the influence of individual elements on the energy consumption. Afterwards, the southern part of Nieuwegein has been modeled by using the microscopic traffic simulator VISSIM. The relationship between all individual elements for three driving styles has been measured by using the energy consumption model. Furthermore, different scenarios have been tested to measure the influence of traffic intensity, winter and eco-driving strategies, resulting in qualitative insights in energy consumption and travel time. A power breakdown graph gives more insight in the specific energy consumption in different scenarios. Afterwards, a generalized cost model has been used to measure the influence of different scenarios and travel time preferences on the optimal routes. A significant influence of these scenarios and preferences on the route choice and energy consumption has been found. The model has been satisfyingly validated by using laboratory measurements and 30 driving tests in Nieuwegein, using a BMW i3.